Data Labeling with LLMs Market Outlook

According to our latest research, the global Data Labeling with LLMs market size was valued at USD 2.14 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 22.8% from 2025 to 2033, reaching a forecasted value of USD 16.6 billion by 2033. This impressive growth is primarily driven by the increasing adoption of large language models (LLMs) to automate and enhance the efficiency of data labeling processes across various industries. As organizations continue to invest in AI and machine learning, the demand for high-quality, accurately labeled datasets—essential for training and fine-tuning LLMs—continues to surge, fueling the expansion of the data labeling with LLMs market.

One of the principal growth factors for the data labeling with LLMs market is the exponential increase in the volume of unstructured data generated by businesses and consumers worldwide. Organizations are leveraging LLMs to automate the labeling of vast datasets, which is essential for training sophisticated AI models. The integration of LLMs into data labeling workflows is not only improving the speed and accuracy of the annotation process but also reducing operational costs. This technological advancement has enabled enterprises to scale their AI initiatives more efficiently, facilitating the deployment of intelligent applications across sectors such as healthcare, automotive, finance, and retail. Moreover, the continuous evolution of LLMs, with capabilities such as zero-shot and few-shot learning, is further enhancing the quality and context-awareness of labeled data, making these solutions indispensable for next-generation AI systems.

Another significant driver is the growing need for domain-specific labeled datasets, especially in highly regulated industries like healthcare and finance. In these sectors, data privacy and security are paramount, and the use of LLMs in data labeling processes ensures that sensitive information is handled with the utmost care. LLM-powered platforms are increasingly being adopted to create high-quality, compliant datasets for applications such as medical imaging analysis, fraud detection, and customer sentiment analysis. The ability of LLMs to understand context, semantics, and complex language structures is particularly valuable in these domains, where the accuracy and reliability of labeled data directly impact the performance and safety of AI-driven solutions. This trend is expected to continue as organizations strive to meet stringent regulatory requirements while accelerating their AI adoption.

Furthermore, the proliferation of AI-powered applications in emerging markets is contributing to the rapid expansion of the data labeling with LLMs market. Countries in Asia Pacific and Latin America are witnessing significant investments in digital transformation, driving the demand for scalable and efficient data annotation solutions. The availability of cloud-based data labeling platforms, combined with advancements in LLM technologies, is enabling organizations in these regions to overcome traditional barriers such as limited access to skilled annotators and high operational costs. As a result, the market is experiencing robust growth in both developed and developing economies, with enterprises increasingly recognizing the strategic value of high-quality labeled data in gaining a competitive edge.

From a regional perspective, North America currently dominates the data labeling with LLMs market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology companies, advanced research institutions, and a mature AI ecosystem. However, Asia Pacific is expected to witness the highest CAGR during the forecast period, driven by rapid digitalization, government initiatives supporting AI development, and a burgeoning startup ecosystem. Europe is also emerging as a key market, with strong demand from sectors such as automotive and healthcare. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their market presence, supported by growing investments in AI infrastructure and talent development.

1. Overview
2. Unsupervised Learning: For the training data required in unsupervised learning, Nexdata delivers data collection and cleaning services for both single-modal and cross-modal data. We provide Large Language Model(LLM) Data cleaning and personnel support services based on the specific data types and characteristics of the client's domain.

-SFT: Nexdata assists clients in generating high-quality supervised fine-tuning data for model optimization through prompts and outputs annotation.

-Red teaming: Nexdata helps clients train and validate models through drafting various adversarial attacks, such as exploratory or potentially harmful questions. Our red team capabilities help clients identify problems in their models related to hallucinations, harmful content, false information, discrimination, language bias and etc.

-RLHF: Nexdata assist clients in manually ranking multiple outputs generated by the SFT-trained model according to the rules provided by the client, or provide multi-factor scoring. By training annotators to align with values and utilizing a multi-person fitting approach, the quality of feedback can be improved.

1. Our Capacity -Global Resources: Global resources covering hundreds of languages worldwide

-Compliance: All the Large Language Model(LLM) Data is collected with proper authorization

-Quality: Multiple rounds of quality inspections ensures high quality data output

-Secure Implementation: NDA is signed to gurantee secure implementation and data is destroyed upon delivery.

-Efficency: Our platform supports human-machine interaction and semi-automatic labeling, increasing labeling efficiency by more than 30% per annotator. It has successfully been applied to nearly 5,000 projects.

3.About Nexdata Nexdata is equipped with professional data collection devices, tools and environments, as well as experienced project managers in data collection and quality control, so that we can meet the Large Language Model(LLM) Data collection requirements in various scenarios and types. We have global data processing centers and more than 20,000 professional annotators, supporting on-demand Large Language Model(LLM) Data annotation services, such as speech, image, video, point cloud and Natural Language Processing (NLP) Data, etc. Please visit us at https://www.nexdata.ai/?source=Datarade

Please share your suggestions to improve my datasets further✍️

📄 Dataset Overview This dataset contains Google Play Store app reviews labeled for sentiment using a deterministic Large Language Model (LLM) classification pipeline. Each review is tagged as positive, negative, or neutral, making it ready for NLP training, benchmarking, and market insight generation.

⚙️ Data Collection & Labeling Process Source: Reviews collected from Google Play Store using the google_play_scraper library. Labeling: Reviews classified by a Hugging Face Transformers-based LLM with a strict prompt to ensure one-word output. Post-processing: Outputs normalized to the three sentiment classes.

💡 Potential Uses Fine-tuning BERT, RoBERTa, LLaMA, or other transformer models. Sentiment dashboards for product feedback monitoring. Market research on user perception trends. Benchmark dataset for text classification experiments.

Please upvote!!!!

This is the augmented data and labels used in training the model, it is also needed for evaluation as the vectoriser is fit on this data and then the test data is transformed on that vectoriser

LLM Data Quality Assurance Market Outlook

According to our latest research, the Global LLM Data Quality Assurance market size was valued at $1.25 billion in 2024 and is projected to reach $8.67 billion by 2033, expanding at a robust CAGR of 23.7% during 2024–2033. The major factor propelling the growth of the LLM Data Quality Assurance market globally is the rapid proliferation of generative AI and large language models (LLMs) across industries, creating an urgent need for high-quality, reliable, and bias-free data to fuel these advanced systems. As organizations increasingly depend on LLMs for mission-critical applications, ensuring the integrity and accuracy of training and operational data has become indispensable to mitigate risk, enhance performance, and comply with evolving regulatory frameworks.

Regional Outlook

North America currently commands the largest share of the LLM Data Quality Assurance market, accounting for approximately 38% of the global revenue in 2024. This dominance can be attributed to the region’s mature AI ecosystem, significant investments in digital transformation, and the presence of leading technology firms and AI research institutions. The United States, in particular, has spearheaded the adoption of LLMs in sectors such as BFSI, healthcare, and IT, driving the demand for advanced data quality assurance solutions. Favorable government policies supporting AI innovation, a strong startup culture, and robust regulatory guidelines around data privacy and model transparency have further solidified North America’s leadership position in the market.

Asia Pacific is emerging as the fastest-growing region in the LLM Data Quality Assurance market, with a projected CAGR of 27.4% from 2024 to 2033. This rapid growth is driven by escalating investments in AI infrastructure, increasing digitalization across enterprises, and government-led initiatives to foster AI research and deployment. Countries such as China, Japan, South Korea, and India are witnessing exponential growth in LLM adoption, especially in sectors like e-commerce, telecommunications, and manufacturing. The region’s burgeoning talent pool, combined with a surge in AI-focused venture capital funding, is fueling innovation in data quality assurance platforms and services, positioning Asia Pacific as a major future growth engine for the market.

Emerging economies in Latin America and the Middle East & Africa are also starting to recognize the importance of LLM Data Quality Assurance, but adoption remains at a nascent stage due to infrastructural limitations, skill gaps, and budgetary constraints. These regions are gradually overcoming barriers as multinational corporations expand their operations and local governments launch digital transformation agendas. However, challenges such as data localization requirements, fragmented regulatory landscapes, and limited access to cutting-edge AI technologies are slowing widespread adoption. Despite these hurdles, localized demand for data quality solutions in sectors like banking, retail, and healthcare is expected to rise steadily as these economies modernize and integrate AI-driven workflows.

Report Scope

Hola: Multilingual-Text-Generation-(LLM) and Language-Classification-Public-Dataset

Release Date: 2/12/2024 (Myanmar Union Day)

   Overview 

Hola dataset contains data from 11 languages, including English, Burmese, Japanese, Spanish, Chinese (Traditional), Korean, Mon, Paoh, etc. And the data was crawled from Wikipedia. Each sample is a sentence data with a label of ISO 639-1 code of respective language, e.g. en, my, ja, es, zh, ko, etc.

   Data 

Each training… See the full description on the dataset page: https://huggingface.co/datasets/simbolo-ai/hola.

This document outlines the process used to create a structured, analyzable dataset of LLM attack methods from a corpus of unstructured red-teaming writeups using https://www.kaggle.com/datasets/kaggleqrdl/red-teaming-all-writeups dataset.

1. Framework Creation

The foundation of this analysis is a formal, hierarchical taxonomy of known LLM attack methods, which is defined in attack_taxonomy.md. This taxonomy provides a controlled vocabulary for classification, ensuring consistency across all entries. The raw, unstructured summaries of various attack methodologies were compiled into a single file, condensed_methods.md.

2. Automated Labeling and Data Enrichment

To bridge the gap between the unstructured summaries and the formal taxonomy, we developed predict_taxonomy.py. This script automates the labeling process:

1. It iterates through each attack summary in condensed_methods.md.
2. For each summary, it calls the Gemini API with a specialized prompt. This prompt instructs the model to act as an expert AI security researcher.
3. The model is provided with both the attack summary and the complete attack_taxonomy.md as context.
4. It is then tasked with selecting the most relevant and specific attack categories that describe the methodology in the summary.

3. Producing the Enriched Dataset

The script captures the list of predicted taxonomy labels from Gemini for each writeup. It then combines the original source, the full summary content, and the new taxonomy labels into a single, structured record.

This entire collection is saved as predicted_taxonomy.json, creating an enriched dataset where each attack method is now machine-readable and systematically classified. This structured data is invaluable for quantitative analysis, pattern recognition, and further research into LLM vulnerabilities.

Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification

This dataset contains two subsets of labeled website data, specifically created to enhance the performance of Homepage2Vec, a multi-label model for website classification. The datasets were generated using Large Language Models (LLMs) to provide more accurate and diverse topic annotations for websites, addressing a limitation of existing Homepage2Vec training data.

Key Features:

LLM-generated annotations: Both datasets feature website topic labels generated using LLMs, a novel approach to creating high-quality training data for website classification models.

Improved multi-label classification: Fine-tuning Homepage2Vec with these datasets has been shown to improve its macro F1 score from 38% to 43% evaluated on a human-labeled dataset, demonstrating their effectiveness in capturing a broader range of website topics.

Multilingual applicability: The datasets facilitate classification of websites in multiple languages, reflecting the inherent multilingual nature of Homepage2Vec.

Dataset Composition:

curlie-gpt3.5-10k: 10,000 websites labeled using GPT-3.5, context 2 and 1-shot

curlie-gpt4-10k: 10,000 websites labeled using GPT-4, context 2 and zero-shot

Intended Use:

Fine-tuning and advancing Homepage2Vec or similar website classification models

Research on LLM-generated datasets for text classification tasks

Exploration of multilingual website classification

Additional Information:

Project and report repository: https://github.com/CS-433/ml-project-2-mlp

Acknowledgments:

This dataset was created as part of a project at EPFL's Data Science Lab (DLab) in collaboration with Prof. Robert West and Tiziano Piccardi.

Data Source: twitterapi.io

Data Acquisition Steps: First, search for 250,000 US financial influencers on Twitter through the specified keywords via the API provided by twitterapi.io. Subsequently, utilize a Large Language Model (LLM) to label these influencers. The LLM assesses the probability of each individual being a financial influencer. In the dataset table, there is a field named "llm_result" which can take on the values of 1, 2, 3, or 4. Notably, a value of 3 or 4 in the "llm_result" field largely indicates that the individual is involved in the financial sector.

1. 150,000 hours TV videos data Data content: 150,000 hours high-quality TV videos, with legitimate copyright and metadata like titles

Data distribution: covers various video types including TV dramas and self-produced programs Data quality: complete video content with audio and resolutions available in both 1080P and 4K, free from watermarks, mosaics and other noise

1. 20 Million high-quality video data Data content: 20 million high-quality videos captured by photographers, with legitimate copyright, including labels and captions in Chinese and English as metadata Data distribution: covers various video contents such as portraits, animals, plants, aerial shots, landscapes, urban scenes, and supports filtering by keywords such as subject, background, motion, cinematography, rendered video Data quality: complete video content with resolutions available in both 1080P and 4K, free from watermarks, mosaics, and other noise

2. 250 Million high-quality image data Data content: 250 million high-quality images captured by photographers, with legitimate copyright, including labels and captions in Chinese and English as metadata Data distribution: covers various image contents such as portraits, animals, plants, food, landscapes and Chinese elements, as well as multiple image types like illustrations and vector graphics Data quality: complete image content with resolutions available in both 1080P and 4K, free from watermarks, mosaics, and other noise

3. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of computer vision data. These ready-to-go Machine Learning (ML) Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/llm?source=Datarade

Bitext - Media Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [media] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-media-llm-chatbot-training-dataset.

Title:

Cleaned and Optimized Dataset for AI vs. Human Text Classification

Overview:

This dataset is a curated and optimized collection of text data designed for training and evaluating machine learning models to distinguish between AI-generated and human-written text. The dataset has been meticulously cleaned, deduplicated, and reduced in size to ensure efficiency while maintaining its utility for research and development purposes.

By combining multiple high-quality sources, this dataset provides a diverse range of text samples, making it ideal for tasks such as binary classification (AI vs. Human) and other natural language processing (NLP) applications.

Key Features:

1. Cleaned Text:

   • All text entries have been preprocessed to remove unwanted characters, extra spaces, and special symbols.
   • Text cleaning ensures consistency and improves model performance by focusing on meaningful content.

2. Label Consistency:

   • Each entry is labeled with a binary value (0 for human-written text, 1 for AI-generated text).
   • Labels have been standardized across all sources for seamless integration.

3. Memory Optimization:

   • The dataset has been optimized to reduce memory usage:
     • Unnecessary columns have been removed.
     • Data types have been downcast to more efficient formats (e.g., category for categorical columns).

4. Deduplication:

   • Duplicate rows have been removed to prevent redundancy and ensure the dataset's integrity.

5. Null Value Handling:

   • Rows with missing or null values have been carefully handled to maintain data quality.

6. Compact Size:

   • The dataset includes only two essential columns: label and clean_text, making it lightweight and easy to use.

Dataset Structure:

The final dataset contains the following columns:

Sources Used:

This dataset is a consolidation of multiple high-quality datasets from various sources, ensuring diversity and representativeness. Below are the details of the sources used:

1. Source 1:

   • Link: https://www.kaggle.com/datasets/shanegerami/ai-vs-human-text
   • Description: A dataset containing AI-generated and human-written text samples.
   • Columns Used: text, generated (renamed to label).

2. Source 2:

   • Link: https://www.kaggle.com/datasets/jdragonxherrera/augmented-data-for-llm-detect-ai-generated-text
   • Description: Augmented data specifically designed for detecting AI-generated text.
   • Columns Used: text, label.

3. Source 3:

   • Link: https://www.kaggle.com/datasets/dardodel/4k-mixtral87b-crafted-essays-for-detect-ai-comp
   • Description: Essays generated by the Mixtral 8x7B model for AI detection competitions.
   • Columns Used: AI_Essay (renamed to text), prompt_id (renamed to label).

4. Source 4:

   • Link: https://www.kaggle.com/datasets/thedrcat/daigt-v2-train-dataset
   • Description: A large dataset of AI and human text samples for training detection models.
   • Columns Used: text, label.

5. Source 5:

   • Link: https://huggingface.co/datasets/artem9k/ai-text-detection-pile (Hugging Face Dataset)
   • Description: A diverse collection of text samples from the Hugging Face Hub.
   • Columns Used: text, source (renamed to label).

6. Source 6:

   • Link: https://www.kaggle.com/datasets/jdragonxherrera/augmented-data-for-llm-detect-ai-generated-text
   • Description: Test data for evaluating AI-generated text detection models.
   • Columns Used: text, label.

Data Cleaning and Preprocessing Steps:

To ensure the dataset is clean, consistent, and optimized for use, the following steps were performed:

1. Column Standardization:

   • Renamed columns across all sources to ensure uniformity (text and label).

2. Text Cleaning:

   • Converted all text to lowercase.
   • Removed non-alphabetic characters, extra spaces, and leading/trailing spaces.

3. Duplicate Removal:

   • Identified and removed duplicate rows to avoid redundancy.

4. Null Value Handling:

   • Dropped rows with missing or null values in critical columns (text or label).

5. Memory Optimization:

   • Converted categorical columns to the category type for memory efficiency.

6. Final Dataset Creation:

   • Retained only the essential columns: label and clean_text.

Use Cases:

This datase...

Bitext - Hospitality Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [hospitality] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-hospitality-llm-chatbot-training-dataset.

1. Specifications Product : Biometric Data

Data size : 200,000 ID

Race distribution : black people, Caucasian people, brown(Mexican) people, Indian people and Asian people

Gender distribution : gender balance

Age distribution : young, midlife and senior

Collecting environment : including indoor and outdoor scenes

Data diversity : different face poses, races, ages, light conditions and scenes Device : cellphone

Data format : .jpg/png

Accuracy : the accuracy of labels of face pose, race, gender and age are more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Speech Data and 800TB of Imagery Data. These ready-to-go Machine Learning(ML) Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

A JSON file with ground truth sentiment labels used in evaluation and comparison to LLM prediction.

This is an LLM-generated external dataset for the: - The Learning Agency Lab - PII Data Detection Competition

Versions

• v2: Added +1000 texts with new PII information like URLs and usernames. Also, now the dataset includes the PII information as columns. Note that not all the PII information is included on the text on purpose.

Description

It contains 3382 4434 generated texts with their corresponding annotated labels in the required competition format.

Description: - document (str): ID of the essay - full_text (string): AI generated text. - tokens (string): a list with the tokens (comes from text.split()) - trailing_whitespace (list): a list with boolean values indicating whether each token is followed by whitespace. - labels (list): list with token labels in BIO format

The VAD dataset contains a total of 29000 data samples, stored in JSON format files. Each JSON object has five attributes, namely instruction, input, output, source, and label, which correspond to the user's instruction, supplementary input for the task, LLM generated reply, data source, and data classification, respectively. Among them, the data source includes human glm4、alpaca3， Corresponding to manually constructed data, large model Chat-GLM4 generation, and Alpaca data translation. The data classification label includes person social、nation， The categories of the data content are individual, social, and national.

The data in this tutorial comes from a multi-label classification bot that categorizes movies into genres based on their names and short descriptions. For this use case, we have an evaluation set containing movies released before 2021, with their associated genres selected by movie critics, as well as production data containing movies released after 2021.